(https://www.coursera.org/learn/machine-learning)
image = Image('resources/hastie_book.jpg')#, width=700)
sh = display(image)
(Hastie et al, 2001)
image = Image('resources/Supervised-Learning-versus-Unsupervised-Learning-Mathworks-nd.png', width=800)
sh = display(image)
(Bunker & Thabtah, 2017)
image = Image('resources/expert_system.png', width=800)
sh = display(image)
(www.igcseict.info/theory/7_2/expert/)
image = Image('resources/classification_versus_regression.png', width=700)
sh = display(image)
(https://blog.statsbot.co/machine-learning-algorithms-183cc73197c)
image = Image('resources/clustering_versus_classification.png', width=700)
sh = display(image)
(https://deepcast.ai/media/article3/)
image = Image('resources/deeplearning1.png', width=700)
sh = display(image)
(https://www.ibm.com/blogs/systems/deep-learning-performance-breakthrough/)
image = Image('resources/deep_learning_growth.jpg', width=600)
sh = display(image)
(Jiang et al, 2017)
image = Image('resources/black_box.png', width=800)
sh = display(image)
(callingbullshit.org/case_studies/case_study_ml_sexual_orientation_original_version.html)
image = Image('resources/tools_used_in_ai.jpg', width=700)
sh = display(image)
(Jiang et al, 2017)
image = Image('resources/data_stress.jpg', width=700)
sh = display(image)
(https://the-modeling-agency.com/data-messy-dont-panic/)
(this is very important - come back to it! - key difference with deep learning)
image = Image('resources/feature_extraction.png', width=700)
sh = display(image)
image = Image('resources/train_validate_test.png', width=600)
sh = display(image)
(https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7)
image = Image('resources/tools_used_in_ai.jpg', width=700)
sh = display(image)
(Jiang et al, 2017)
It depends a lot on the data and what you are trying to learn. Quite standard practice to fit a simple model (e.g logistic regression) and a more powerful mode (e.g. random forest) and see which works best.
(https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7)
image = Image('resources/clustering_versus_classification.png', width=700)
sh = display(image)
image = Image('resources/subgroups_table.png') #, width=1500)
sh = display(image)
(Corrigan, Harush, Morgan, Shelim, Zulkarnaen, 2018)
Data: Physionet Challenge 2012
(https://physionet.org/challenge/2012/)
image = Image('resources/student_clusters.png') #, width=1500)
sh = display(image)
image = Image('resources/student_table.png', width=500)
sh = display(image)
image = Image('resources/nld_criteria_original.png')
sh = display(image)
image = Image('resources/codified_nld.png')
sh = display(image)
## thanks to: https://stackoverflow.com/questions/47637739/how-to-display-two-local-images-side-by-side-in-jupyter
## and border removal, thanks God! : https://github.com/ipython/ipython/issues/8581
display(HTML("<table style='border: 0'><tr style='border: 0'><td style='border: 0'><p style='border: none!important;'><img src='resources/time_panel_survivor_ptassess.png'></td><td style='border: 0'><img src='resources/time_panel_survivor_labres.png'></td></tr></table>"))
| ![]() |
display(HTML("<table><tr><td><img src='resources/time_panel_survivor2_ptassess.png'></td><td><img src='resources/time_panel_survivor2_labres.png'></td></tr></table>"))
![]() | ![]() |
display(HTML("<table><tr><td><img src='resources/time_panel_mortality_ptassess.png'></td><td><img src='resources/time_panel_mortality_labres.png'></td></tr></table>"))
![]() | ![]() |
image = Image('resources/comparison_table.png')
sh = display(image)
image = Image('resources/cohort_table.png')
sh = display(image)
image = Image('resources/clustering_versus_classification.png', width=700)
sh = display(image)
image = Image('resources/tsne.png') #, width=1500)
sh = display(image)
image = Image('resources/figure1.png', width=1500)
sh = display(image)
image = Image('resources/performance_table_imputed.png')
sh = display(image)
image = Image('resources/fimp_table_imputed.png')
sh = display(image)
image = Image('resources/ebi_logo.png')
sh = display(image)
image = Image('resources/labres_gicu_survivors.png')
sh = display(image)
image = Image('resources/time_of_day_hists.png') #, width=1500)
sh = display(image)
image = Image('resources/time_of_day_patterns.png') #, width=1500)
sh = display(image)
Mention groups in Bristol working on this. (And others?)
image = Image('resources/rfd_board_whole.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_template_only.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_elements.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_1_&_8.png') #, width=1500)
sh = display(image)
image = Image('resources/rfd_board_1_&_8_contoured.png') #, width=1500)
sh = display(image)
image = Image('resources/tensor_flow_example.png', width=700)
sh = display(image)
(https://www.tensorflow.org/)
image = Image('resources/rfd_board_predictions.png') #, width=1500)
sh = display(image)
image = Image('resources/sevens.png') #, width=1500)
sh = display(image)
Take home message: be very sceptical of anyone using neural nets with this little data!
Other deep learning neural net stuff in ICU is out there....
Notably Deepmind Health are NOT doing deep learning....
(Who said this?)
(make some slides on this but probably will skip them)
image = Image('resources/bd2k.jpg', width=700)
sh = display(image)
(Friedman, 2015)
Talk about our deicison support tool for discharge.
And what it could be extended to.
Link to discharge tool...